A new processing technique has been introduced whereby spatial information, such as address data, in large databases can be interpreted by applying spatial context and spatial rules. But since present day spatial data mining requires expensive spatial/geometrical calculations involving a huge amount of data, and since extremely difficult technical problems are frequently encountered, spatial data mining has not been well studied and remains an underdeveloped field. However, spatial data mining is considered to be a feasible basic technique that can greatly assist in the development of databases for the information industry or for the GIS (Geographical Information System) field which have huge volumes of business. Spatial data mining, and associated techniques, is further considered to be a field having the potential to provide many benefits for businesses.
Conventional spatial data mining systems, used for determining distances in advance through the introduction of correlated spatial rules, are well known. According to a method proposed by J. Han, et. al (“Spatial Data Mining: Progress and Challenges”, SIGMOD '96 Data Mining Workshop, pp. 55–69, 1996), for example, distance predicate terms “close to” and “far from” are defined, and correlated spatial rules, including the following two, are introduced from a spatial information database:
“close to a park”→“residential area” (support rate 5%, confidence rate 80%)
“drop in land price”→“far from a station” (support rate 10%, confidence rate 70%)
Further, another conventional spatial data mining system for determining an orientation rule in advance for the introduction of a correlated spatial rule is also well known. According to the above method proposed by J. Han, et. al, spatial orientation terms “west of” and “north of” are defined, and correlated spatial rules, including the following spatial orientation predicated ones, can be introduced from a spatial information database:
“west of a park”→“residential area” (support rate 5%, confidence rate 80%)
“drop in land price”→“north of a station” (support rate 10%, confidence rate 70%).
However, “close to” and “far from”, which are included in the method proposed by J. Han, et. al, must be defined before data mining is initiated by providing a distance, such as “close to X”=“within a distance Y of X” and “far from X”=“farther than a distance Z from X”. In addition, “west of” and “north of” must be defined before data mining is initiated by providing a range and an angle, such as “west of X”=“the inside of a rectangle one side of which, to the west of X, has a length of Y” and “north of X”=“an angle of Y1° to Y2° from X”. At this time, a distance such as Y or Z, which is used for optimizing a specific objective function, or a numerical value for strictly determining an angle such as Y1° or Y2°, which is used for optimizing a specific objective function, is requested by many analyzation businesses, and even when the latest conventional techniques are employed, many of those analyzation businesses can not satisfactorily cope with their operation.
Conventional data mining systems can not, for example, cope with a search for “a radius extending outward from a convenience store used to maximize the installation density of automatic teller machines within a unit distance in a district A” or a search to ascertain “the orientation of a route along which heavy air pollution spreads from a garbage disposal area”.